PARIS: A Real-Time Malicious Behavior Detection System Using Adaptive Trace Fetching
PARIS is a novel system that addresses the limitations of existing malware detection methods by using adaptive trace fetching to enable real-time, low-overhead detection of malicious behavior on Windows systems.
Hybrid Machine Learning Meta-Model for Malware Detection Using Contextual, Behavioral, and Static Analysis
Combining static, behavioral (emulation-based), and contextual analysis in a hybrid machine learning model significantly improves malware detection rates, especially under low false-positive requirements, compared to using individual analysis methods.
Static Malware Detection Using Image and Opcode Feature Fusion and a CNN-BiLSTM Model
Fusing image texture features with opcode sequence features, processed through a CNN-BiLSTM model, significantly improves static malware detection accuracy and robustness compared to traditional and single-feature methods.
Bayesian Learned Models Detect Adversarial Malware For Free
Bayesian models can effectively detect adversarial malware by leveraging uncertainty without sacrificing performance.
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